Intermediate mathematics
See also: Category:Foundations It has been known since the time of Euclid that all of geometry can be derived from a handful of objects (points, lines...), a few actions on those objects, and a small number of axioms. Every field of science likewise can be reduced to a small set of objects, actions, and rules. Math itself is not a single field but rather a constellation of related fields. One way in which new fields are created is by the process of generalization. A generalization is the formulation of general concepts from specific instances by abstracting common properties. Generalization is the process of identifying the parts of a whole, as belonging to the whole. The parts, completely unrelated may be brought together as a group by establishing a common relation between them.Wikipedia:Generalization Foreword Mathematical notation can be extremely intimidating. Wikipedia is full of articles with page after page of indecipherable text. At first glance this article might appear to be the same. I want to assure the reader that every effort has been made to simplify everything as much as possible and to provide all relevant information or, at very least, to make such information easy to find. The following has been assembled from countless small pieces found scattered throughout the world wide web. I cant guarantee that there are no errors in it. Please report any errors or omissions on this articles talk page. Euclids "common notions" :From Wikipedia:Euclidean geometry *Things that are equal to the same thing are also equal to one another ::(formally the Euclidean property of equality, but may be considered a consequence of the transitivity property of equality). *If equals are added to equals, then the wholes are equal ::(Addition property of equality). *If equals are subtracted from equals, then the remainders are equal ::(Subtraction property of equality). *Things that coincide with one another are equal to one another ::(Reflexive Property). *The whole is greater than the part. Numbers :See: Peano axioms The basis of all of mathematics is the "Next" function (see Graph theory). Next(1)=2, Next(2)=3, Next(3)=4...This defines the Natural numbers (denoted \mathbb{N} ). These have the convenient property of being transitive. That means that if a1-3=x for which there is no answer among natural numbers. To provide an answer mathematicians generalize to the set of all integers (denoted \mathbb{Z} ) which includes negative integers and zero. :The Additive identity is zero because x + 0 = x. Multiplication is defined as repeated addition, and its inverse is division. But this leads to equations like 3/2=x for which there is no answer. The solution is to generalize to the set of rational numbers (denoted \mathbb{Q} ). Any number which isnt rational is irrational. :The Multiplicative identity is one because x * 1 = x. :Division by zero is undefined and undefinable. :(Addition and multiplication are fast but division is slow even for computers.) Exponentiation is defined as repeated multiplication, and its inverses are roots and logarithms. But this leads to multiple equations with no solutions: :Equations like \sqrt{2}=x. The solution is to generalize to the set of algebraic numbers (denoted \mathbb{A} ) ::Equations like 2^{\sqrt{2}}=x The solution (because x is transcendental) is to generalize to the set of Real numbers (denoted \mathbb{R} ). :Equations like \sqrt{-1}=x and e^x=-1. The solution is to generalize to the set of complex numbers (denoted \mathbb{C} ) by defining i = \sqrt{-1}. A single complex number z=a+bi consists of a real part a'' and an imaginary part ''bi. ::The Complex conjugate of a complex number z is \bar z=a-bi. (Not to be confused with the dual of a vector.) :It then follows that e^{i \pi}=-1 because e^{ix}=\cos x+i\sin x. See below. :0^0 = 1 Tetration is defined as repeated exponentiation and its inverses are called super-root and super-logarithm. : \begin{matrix} a\uparrow\uparrow b & = {\ ^{b}a} = & \underbrace{a^{a^ }}}} & = & \underbrace{a\uparrow (a\uparrow(\dots\uparrow a))} \\ & & b\mbox{ copies of }a & & b\mbox{ copies of }a \end{matrix} Imaginary numbers (denoted \mathbb{I} ) often occur in equations involving change with respect to time. If friction is resistance to motion then imaginary friction would be resistance to change of motion wrt time. (In other words, imaginary friction would be mass.) In fact, in the equation for the Spacetime interval (given below), time itself is an imaginary quantity. Complex numbers can be used to represent and perform rotations but only in 2 dimensions. Hypercomplex numbers like quaternions (denoted \mathbb{H} ), octonions (denoted \mathbb{O} ), and sedenions (denoted \mathbb{S} ) are one way to generalize complex numbers to some (but not all) higher dimensions. Tensors, on the other hand, can be used in any number of dimensions to represent and perform rotations and other linear transformations. Rotations in n dimensions are called SO(n). See Visualization of Tensor multiplication. :Any affine transformation is equivalent to a linear transformation followed by a translation of the origin. (The origin is always a fixed point for any linear transformation.) "Translation" is just a fancy word for "move". A tensor is a multivector. Understanding how tensors work leads one to geometric algebra. An equation written in geometric algebra is much more intuitive than one written in matrix form. Clifford algebra generalizes geometric algebra to complex space. When a quantity, like the charge of a single electron, becomes so small that it is insignificant we, quite justifiably, treat it as though it were zero. A quantity that can be treated as though it were zero, even though it very definitely is not, is called infinitesimal. If q is a finite ( q \cdot 1 ) amount of charge then using Leibniz's notation dq would be an infinitesimal ( q \cdot 1/\infty ) amount of charge. See Differential Likewise when a quantity becomes so large that a regular finite quantity becomes insignificant then we call it infinite. We would say that the mass of the ocean is infinite ( M \cdot \infty ) . But compared to the mass of the Milky Way galaxy our ocean is insignificant. So we would say the mass of the Galaxy is doubly infinite ( M \cdot \infty^2 ) . Infinity and the infinitesimal are called Hyperreal numbers (denoted {}^*\mathbb{R} ). Hyperreals behave, in every way, exactly like real numbers. For example, 2 \cdot \infty is exactly twice as big as \infty. In reality, the mass of the ocean is a real number so it is hardly surprising that it behaves like one. Vectors :See also: Algebraic geometry, Algebraic variety, Algebraic manifold, and Linear algebra The one dimensional number line can be generalized to a multidimensional Cartesian coordinate system thereby creating multidimensional math (i.e. geometry). : \mathbb{R}^3 is the Cartesian product \mathbb{R} \times \mathbb{R} \times \mathbb{R}. : \mathbb{C}^3 is the Cartesian product \mathbb{C} \times \mathbb{C} \times \mathbb{C} A vector space is a coordinate space with vector addition and scalar multiplication (multiplication of a vector and a scalar belonging to a field. Field is defined below). :If {\mathbf e_1} , {\mathbf e_2} , {\mathbf e_3} are orthogonal unit basis vectors). :and {\mathbf u} , {\mathbf v} , {\mathbf x} are arbitrary vectors then we can (and usually do) write: ::'' \mathbf{u} = u_1 \mathbf{e_1} + u_2 \mathbf{e_2} + u_3 \mathbf{e_3} = \begin{bmatrix} u_1 & u_2 & u_3 \end{bmatrix} '' ::'' \mathbf{v} = v_1 \mathbf{e_1} + v_2 \mathbf{e_2} + v_3 \mathbf{e_3} = \begin{bmatrix} v_1 & v_2 & v_3 \end{bmatrix} '' ::'' \mathbf{x} = x_1 \mathbf{e_1} + x_2 \mathbf{e_2} + x_3 \mathbf{e_3} = \begin{bmatrix} x_1 & x_2 & x_3 \end{bmatrix} '' :A module generalizes a vector space by allowing multiplication of a vector and a scalar belonging to a ring. Ring is defined below. Coordinate systems define the length of vectors parallel to one of the axes but leave all other lengths undefined. This concept of "length" which only works for certain vectors is generalized as the "norm" which works for all vectors. The norm of vector \mathbf{v} is denoted \|\mathbf{v}\|. A Banach space is a normed vector space that is also a complete metric space (there are no points missing from it). :Taxicab metric (see [[Lp space|''L'p'' space]]) :: \|\mathbf{v}\| = v_1 + v_2 + v_3 :In Euclidean space the norm doesnt depend on the choice of coordinate system therefore rigid objects can rotate. See proof of the Pythagorean theorem to the right. :: \|\mathbf{v}\| = \sqrt{v_1^2 + v_2^2 + v_3^2} :In Minkowski space the Spacetime interval is :: \|s\| = \sqrt{x^2 + y^2 + z^2 + (cti)^2} :In complex space the most common norm of an n dimensional vector is obtained by treating it as though it were a regular real valued 2n dimensional vector in Euclidean space :: \left\| \boldsymbol{z} \right\| = \sqrt{z_1 \bar z_1 + \cdots + z_n \bar z_n} A manifold \mathbf{M} is a type of topological space in which each point has an infinitely small neighbourhood that is homeomorphic to Euclidean space. A manifold is locally, but not globally, Euclidean. :A Tangent space \mathbf{T}_p \mathbf{M} is the set of all vectors tangent to \mathbf{M} at point p. :Informally, a tangent bundle \mathbf{TM} (red cylinder in image to the right) on a differentiable manifold \mathbf{M} (blue circle) is obtained by joining all the tangent spaces (red lines) together in a smooth and non-overlapping manner.Wikipedia:Tangent bundle The tangent bundle always has twice as many dimensions as the original manifold. ::A vector bundle is the same thing minus the requirement that it be tangent. A vector bundle is a fiber bundle whose fibers are vector spaces. :::A fiber bundle is a generalization of a vector bundle. :The cotangent bundle (Dual bundle) of a differentiable manifold is obtained by joining all the cotangent spaces. The cotangent bundle always has twice as many dimensions as the original manifold. Sections of that bundle are known as differential one-forms. Multiplication of vectors Multiplication can be generalized to allow for multiplication of vectors in 5 different ways: Outer product (a tensor): \mathbf{u} \otimes \mathbf{v}. :As one would expect, every component of one vector multipies with every component of the other vector. : \begin{align}\mathbf{u} \otimes \mathbf{v} = \begin{bmatrix}u_1 \mathbf{e_1} \\ u_2 \mathbf{e_2} \\ u_3 \mathbf{e_3} \end{bmatrix} \begin{bmatrix}v_1 \mathbf{e_1} & v_2 \mathbf{e_2} & v_3 \mathbf{e_3} \end{bmatrix} = \begin{bmatrix}u_1 v_1 \mathbf{e_1} \mathbf{e_1} & u_1 v_2 \mathbf{e_1} \mathbf{e_2} & u_1 v_3 \mathbf{e_1} \mathbf{e_3} \\ u_2 v_1 \mathbf{e_2} \mathbf{e_1} & u_2 v_2 \mathbf{e_2} \mathbf{e_2} & u_2 v_3 \mathbf{e_2} \mathbf{e_3} \\ u_3 v_1 \mathbf{e_3} \mathbf{e_1} & u_3 v_2 \mathbf{e_3} \mathbf{e_2} & u_3 v_3 \mathbf{e_3} \mathbf{e_3} \end{bmatrix} \end{align} ::The Tensor product generalizes the outer product. The tensor product of a rank n tensor and a rank m tensor results in a rank n+m tensor. Dot product (a Scalar): \mathbf{u}\bullet\mathbf{v} = \|\mathbf{u}\|\ \|\mathbf{v}\|\cos(\theta) = u_1 v_1 + u_2 v_2 + u_3 v_3 : \mathbf{u}\bullet\mathbf{v} = \begin{bmatrix}u_1 \mathbf{e_1} \\ u_2 \mathbf{e_2} \\ u_3 \mathbf{e_3} \end{bmatrix} \begin{bmatrix}v_1 \mathbf{e_1} & v_2 \mathbf{e_2} & v_3 \mathbf{e_3} \end{bmatrix} = \begin{bmatrix}u_1 v_1 \mathbf{e_1}\mathbf{e_1} + u_2 v_2 \mathbf{e_2}\mathbf{e_2} + u_3 v_3 \mathbf{e_3}\mathbf{e_3} \end{bmatrix} :Strangely, only parallel components multiply. (But see below.) In Euclidean space \|\mathbf{v}\|^2 = \mathbf{v}\bullet\mathbf{v}= Q(\mathbf{x}). The dot product of a rank n tensor and a rank m tensor results in a rank n-m tensor. ::The dot product can be generalized to the bilinear form \beta(\mathbf{u,v}) = scalar and its associated quadratic form Q(\mathbf{x}) = \beta(\mathbf{x,x}). Two vectors are orthogonal if \beta(\mathbf{u,v}) = 0. A bilinear form is symmetric if \beta(\mathbf{u,v}) = \beta(\mathbf{v,u}) :::The bilinear form can be further generalized to the inner product (a sesquilinear form) \langle u,v\rangle=\overline{\langle v,u\rangle} ::::A Hilbert space is an inner product space that is also a Complete metric space. Wedge product (a simple bivector): \mathbf{u} \wedge \mathbf{v} = \mathbf{u} \otimes \mathbf{v} - \mathbf{v} \otimes \mathbf{u} = \overline{\mathbf{v}} :The wedge product is also called the exterior product (sometimes mistakenly called the outer product). The term "exterior" comes from the exterior product of two vectors not being a vector. Just as a vector has length and direction so a bivector has an area and an orientation. In three dimensions \mathbf{u} \wedge \mathbf{v} is a pseudovector and its dual is the cross product. \overline{\mathbf{u} \wedge \mathbf{v}} = \mathbf{u} \times \mathbf{v} : \mathbf{u} \wedge \mathbf{u} = 0 : ::The Matrix commutator generalizes the wedge product. :: A_2 = A_1A_2 - A_2A_1 Regressive product: \mathbf{u} \vee \mathbf{v} = \underline{\overline{\mathbf{u}} \wedge \overline{\mathbf{v}}} = \overline{\underline{\mathbf{u}} \wedge \underline{\mathbf{v}}} :The regressive product of two vectors is the dual of the wedge product of the duals of the two vectors. Geometric product (a multivector): \mathbf{u} \mathbf{v} = \mathbf{u} \bullet \mathbf{v} + \mathbf{u} \wedge \mathbf{v} : \mathbf{e_1} \mathbf{e_1} = \mathbf{e_{11}} = \mathbf{e_1} \bullet \mathbf{e_1} + \mathbf{e_1} \wedge \mathbf{e_1} = 1 + 0 = 1 : \mathbf{e_1} \mathbf{e_2} = \mathbf{e_{12}} = \mathbf{e_1} \bullet \mathbf{e_2} + \mathbf{e_1} \wedge \mathbf{e_2} = 0 + \mathbf{e_1} \wedge \mathbf{e_2} = \overline{\mathbf{e_3}} : ^2 Where \epsilon_{\mathbf{a}} is the signature of the vector. |} Integration and differentiation of functions :See also: Hyperreal number The '''integral (antiderivative) is a generalization of multiplication. :For example: an object dropped from point r1 to point r2 will release energy but the usual equation mass \cdot gravity \bullet (r_1 - r_2) = energy cant be used if the strength of gravity is itself a function of radius. The strength of gravity at r1 would be different than it is at r2. And in fact g® = 1/r^2 (See inverse-square law.) :However, the corresponding Definite integral is easily solved: mass \cdot \int_{r_1}^{r_2} g® \cdot dr : :The integral of a function is equal to the area under the curve. When the "curve" is a constant the integral reduces to ordinary multiplication. The derivative is a generalization of division. The derivative of the integral of f(x) is just f(x). The derivative of a function at any point is equal to the slope of the function at that point therefore the derivative of a constant is zero. : {d(k \cdot x^y) \over dx} \quad = \quad k \cdot {d(x^y) \over dx} \quad = \quad k \cdot y \cdot x^{y-1} If we know the value of a smooth function (smooth means all its derivatives are continuous) and the value of all its derivatives at point x=0 then we can determine the value at any other point x'' by using the Maclaurin series. ("!" means factorial) : a_0 x^0 + a_1 x^1 + a_2 x^2 + a_3 x^3 + \cdots :where a_n = {f^{(n)}(0) \over n!} The proof of this is actually quite simple. Plugging in a value of ''x=0 causes all terms but the first to become zero. So, assuming that such a function exists, a0 must be the value of the function at x=0. Simply differentiate the function and repeat for the next term. And so on. ::The Taylor series generalizes this formula. :::The Laurent series generalizes the Taylor series. See Cauchy's integral formula. We can easily determine the Maclaurin series expansion of the exponential function e^x . : e^x = \sum_{n = 0}^{\infty} {x^n \over n!} = {x^0 \over 0!} + {x^1 \over 1!} + {x^2 \over 2!} + {x^3 \over 3!} + {x^4 \over 4!} + \cdots And cos(x) and sin(x) : \cos x = \frac{x^0}{0!} - \frac{x^2}{2!} + \frac{x^4}{4!} - \frac{x^6}{6!} + \cdots : \sin x = \frac{x^1}{1!} - \frac{x^3}{3!} + \frac{x^5}{5!} - \frac{x^7}{7!} + \cdots The Maclaurin series cant be used for a discontinuous function like a square wave because it is not differentiable but remarkably we can use the Fourier series to expand it or any other periodic function of period p'' (and frequency f) into an infinite sum of sine waves (of frequency nf) each of which is an infinite sum of terms each one of which is differentiable! : s(t) = \frac{a_0}{2} + \sum_{n=1}^\infty \lefta_n\cos\left(nt\right)+b_n\sin\left(nt\right)\right : a_n = \frac{2}{p}\int_{t_0}^{t_p} s(t)\cdot \cos\left(\tfrac{2\pi nt}{p}\right)\ dt : b_n = \frac{2}{p}\int_{t_0}^{t_p} s(t)\cdot \sin\left(\tfrac{2\pi nt}{p}\right)\ dt ::The reason this works is that ∫ fn*(f1+f2+f3+...) = ∫ (fn*f1) + ∫ (fn*f2) + ∫ (fn*f3) +....and multiplying any 2 sine waves of frequency n1f and frequency n2f (of period p/n1 and p/n2) and integrating over one period p will always equal zero unless n1=n2. See the graph of sin2(x) to the right. :A 2 dimensional Fourier series is used in video compression. :In mathematical analysis, many generalizations of Fourier series have proven to be useful. They are all special cases of decompositions over an orthonormal basis of an inner product space.Wkikpedia:Generalized Fourier series :Spherical harmonics are a complete set of orthogonal functions on the sphere, and thus may be used to represent functions defined on the surface of a sphere, just as circular functions (sines and cosines) are used to represent functions on a circle via Fourier series.Wikipedia:Spherical harmonics Spherical harmonics are basis functions for SO(3). Fourier transforms generalize Fourier series to nonperiodic functions like a single pulse of a square wave. The more localized in the time domain (the shorter the pulse) the more the Fourier transform is spread out across the frequency domain and vice versa, a phenomenon known as the uncertainty principle. The Fourier transform of the Dirac delta function gives G(f)=1 : G(f)=\mathcal{F}\{f,s(t)\}=\int_{-\infty}^\infty e^{-2\pi ift}s(t)dt is a typical transient response]] :Laplace transforms generalize Fourier transforms to complex frequency. Complex frequency includes a term corresponding to the amount of damping. ::Integral transforms generalize Laplace transforms to other kernals (besides sine and cosine) Partial derivatives 'Partial derivatives''' and multiple integrals generalize derivatives and integrals to multiple dimensions. The partial derivative with respect to one variable \frac{\part f(x,y)}{\part x} is found by simply treating all other variables as though they were constants. Multiple integrals are found the same way. Let f(x, y, z) be a scalar function (for example electric potential energy or temperature). A 2 dimensional example of a scalar function would be an elevation map. (Contour lines of an elevation map are an example of a level set.) The Gradient of f(x, y, z) is a vector field whose value at each point is a vector (technically its a covector because it has units of distance-1) that points "downhill" with a magnitude equal to the slope of the function at that point. You can think of it as how much the function changes per unit distance. For static (unchanging) fields the Gradient of the electric potential is the electric field itself. The gradient of temperature gives heat flow. : \operatorname{grad}(f) = \nabla f = \frac{\partial f}{\partial x} \mathbf{i} + \frac{\partial f}{\partial y} \mathbf{j} + \frac{\partial f}{\partial z} \mathbf{k} = \mathbf{F} The Divergence of a vector field is a scalar. The divergence of the electric field is non-zero wherever there is electric charge and zero everywhere else. Field lines begin and end at charges. In fact the charges create the electric field. : \operatorname{div}\,\mathbf{F} = \nabla\cdot\mathbf{F} = \left( \frac{\partial}{\partial x}, \frac{\partial}{\partial y}, \frac{\partial}{\partial z} \right) \cdot (F_x,F_y,F_z) = \frac{\partial F_x}{\partial x} +\frac{\partial F_y}{\partial y} +\frac{\partial F_z}{\partial z}. The Laplacian is the divergence of the gradient of a function: : \Delta f = \nabla^2 f = (\nabla \cdot \nabla) f = \frac{\partial^2 f}{\partial x^2} + \frac{\partial^2 f}{\partial y^2} + \frac{\partial^2 f}{\partial z^2}. The curl of a vector field describes how much the vector field is twisted. (The field may even go in circles.) The curl at a certain point of a magnetic field is the current vector at that point. In fact current creates the magnetic field. :curl( \mathbf{F} ) = \nabla \times \mathbf{F} = \begin{vmatrix} \mathbf{i} & \mathbf{j} & \mathbf{k} \\ {\frac{\partial}{\partial x}} & {\frac{\partial}{\partial y}} & {\frac{\partial}{\partial z}} \\ F_x & F_y & F_z \end{vmatrix} :curl( \mathbf{F} ) = \left(\frac{\partial F_z}{\partial y} - \frac{\partial F_y}{\partial z}\right) \mathbf{i} + \left(\frac{\partial F_x}{\partial z} - \frac{\partial F_z}{\partial x}\right) \mathbf{j} + \left(\frac{\partial F_y}{\partial x} - \frac{\partial F_x}{\partial y}\right) \mathbf{k} The curl of the gradient of any scalar field is always zero. The curl of a vector field in 4 dimensions would no longer be a vector. It would a bivector. However the curl of a bivector field in 4 dimensions would still be a vector. See also: differential forms. The Gradient of a vector field is a tensor field. Each row is the gradient of the corresponding scalar function: : \nabla \mathbf{F} = \begin{bmatrix} \frac{\partial F_1}{\partial x} & \frac{\partial F_1}{\partial y} & \frac{\partial F_1}{\partial z}\\ \frac{\partial F_2}{\partial x} & \frac{\partial F_2}{\partial y} & \frac{\partial F_2}{\partial z} \\ \frac{\partial F_3}{\partial x} & \frac{\partial F_3}{\partial y} & \frac{\partial F_3}{\partial z} \end{bmatrix} The Lie derivative generalizes the Lie bracket which generalizes the wedge product which is a generalization of the cross product which only works in 3 dimensions. The cross product is neither commutative nor associative and therefore doesnt form a field or even a ring (see below). Instead it forms a Lie algebra (See Infinitesimal transformation) which is a local or linearized version of a Lie group. A Lie group is a group that is also a differentiable manifold. Holomorphic functions :See also: Cauchy's integral formula and Wikipedia:potential flow The Cauchy–Riemann conditions are a set of partial differential equations which, along with certain other criteria, guarantee a complex function will be holomorphic (that is, complex differentiable). The formula for the derivative of a complex function f'' at a point ''z0 is the same as for a real function: : f'(z_0) = \lim_{z \to z_0} {f(z) - f(z_0) \over z - z_0 }. For every holomorphic function f(z) = u(x, y) + i v(x, y) both u and v are harmonic functions. :v is the harmonic conjugate of u. ::Geometrically u and v are related as having orthogonal trajectories, away from the zeroes of the underlying holomorphic function; the contours on which u and v are constant cross at right angles. In this regard, u+iv would be the complex potential, where u is the potential function and v is the stream function.Wikipedia:Harmonic conjugate :u and v are both solutions of Laplace's equation \nabla^2 f = 0 so divergence of the gradient is zero :A harmonic function is a scalar potential function therefore the curl of the gradient will also be zero. See Potential theory :Any two-dimensional harmonic function is the real part of a complex analytic function. See complex analysis.Wikipedia:Potential theory Cauchy's integral formula states that the value of a holomorphic scalar potential function within a disc is determined entirely by the values of the function on the boundary of the disc. Divergence can be nonzero outside the disc. :Cauchy's integral formula can be generalized to more than two dimensions. Generalization of addition and multiplication :Main articles: Algebraic structure and Abstract algebra Addition and multiplication can be generalized in so many ways that mathematicians have created a whole system of categories just to organize them. Morphisms :See also: Higher category theory and Multivalued function (misnomer) Every function has exactly one output for every input. If the function is invertible then its inverse function has exactly one output for every input. If it isn't invertible then it doesn't have an inverse function. A morphism is exactly the same as a function but in Category theory every morphism has an inverse which is allowed to have more than one value or no value at all. Categories consist of: :Objects (usually Sets) :Morphisms (usually maps) possessing: ::one source object (domain) ::one target object (codomain) a morphism is represented by an arrow: : f(x)=y is written f : x \to y where x is in X and y is in Y. : g(y)=z is written g : y \to z where y is in Y and z is in Z. The image of y is z. The preimage (or fiber) of z is the set of all y whose image is z and is denoted g^{-1}z A space Y is a covering space (a fiber bundle) of space Z if the map g : y \to z is locally homeomorphic. :A covering space is a universal covering space if it is simply connected. ::The concept of a universal cover was first developed to define a natural domain for the analytic continuation of an analytic function. A topological space is (path-)connected if no part of it is disconnected. A space is simply connected if there are no holes passing all the way through it (therefore any loop can be shrunk to a point) Composition of morphisms: : g(f(x)) is written g \circ f ::f is the pullback of g ::f is the lift of g \circ f ::? is the pushforward of ? A homomorphism is a map from one set to another of the same type which preserves the operations of the algebraic structure: : f(x \bullet y) = f(x) \bullet f(y) : f(x + y) = f(x) + f(y) ::A Functor is a homomorphism with a domain in one category and a codomain in another. A Multicategory has morphisms with more than one source object. A Multilinear map f(v_1,\ldots,v_n) = W : : f\colon V_1 \times \cdots \times V_n \to W\text{,} has a corresponding Linear map: F(v_1\otimes \cdots \otimes v_n) = W : : F\colon V_1 \otimes \cdots \otimes V_n \to W\text{,} Set theory \varnothing is the empty set (the additive identity) \mathbf{U} is the universe of all elements (the multiplicative identity) a \in A means that is a element (or member) of set . In other words a is in A. : \{ x \in \mathbf{A} : x \notin \mathbb{R} \} means the set of all x's that are members of the set such that x is not a member of the reals. Could also be written \{ \mathbf{A} - \mathbb{R} \} A set does not allow multiple instances of an element. \{1,1,2\} = \{1,2\} :A multiset does allow multiple instances of an element. \{1,1,2\} \neq \{1,2\} A \subset B means that is a proper subset of : A \subseteq A means that is a subset of itself. But a set is not a proper subset of itself. A \cup B is the Union of the sets and . In other words, \{A+B\} : \{1,2\}+\{2,3\}=\{1,2,3\} A \cap B is the Intersection of the sets and . In other words, \{A \bullet B\} All a's in B. :Associative: A \bullet \{B \bullet C\} = \{A \bullet B\} \bullet C :Distributive: A \bullet \{B + C\}=\{A \bullet B\} + \{A \bullet C\} :Commutative: \{A \bullet B\} =\{B \bullet A\} A \setminus B is the Set difference of and . In other words, \{A - A \bullet B\} : \overline{A} or A^c = \{U - A\} is the complement of A. A \bigtriangleup B or A \ominus B is the Anti-intersection of sets and which is the set of all objects that are a members of either or but not in both. : A \bigtriangleup B = (A + B) - (A \bullet B) = (A - A \bullet B) + (B - A \bullet B) A \times B is the Cartesian product of and which is the set whose members are all possible ordered pairs where is a member of and is a member of . The Power set of a set is the set whose members are all of the possible subsets of . \exists means "there exists at least one" \exists! means "there exists one and only one" \forall means "for all" \land means "and" (not to be confused with wedge product) \lor means "or" (not to be confused with antiwedge product) Probability \vert A \vert is the cardinality of A which is the number of elements in A. P(A) = {\vert A \vert \over \vert U \vert} is the unconditional probability that A will happen. P(A \mid B) = {\vert A \bullet B \vert \over \vert B \vert} is the conditional probability that A will happen given that B has happened. P(A + B) = P(A) + P(B) - P(A \bullet B) means that the probability that A'' ''or B'' will happen is the probability of ''A plus the probability of B'' minus the probability that both ''A and B'' will happen. P(A \bullet B) = P(A \bullet B \mid B)P(B) = P(A \bullet B \mid A)P(A) means that the probability that ''A and B will happen is the probability of "A and B given B" times the probability of B. P(A \bullet B \mid B) = \frac{P(A \bullet B \mid A) \, P(A)}{P(B)}, is Bayes' theorem Strategic thinking :From Wikipedia:Game theory In the accompanying example there are two players; one chooses the row and the other chooses the column. The payoffs are provided in the interior. The first number is the payoff received by Player 1; the second is the payoff for Player 2. Each player acts without knowing the actions of the other. In zero-sum games the total benefit to all players in the game, for every combination of strategies, always adds to zero (more informally, a player benefits only at the equal expense of others). Tit for tat is an English saying meaning "equivalent retaliation". It is also a highly effective strategy in game theory for the iterated prisoner's dilemma. The strategy was first introduced by Anatol Rapoport in Robert Axelrod's two tournaments, held around 1980. Notably, it was (on both occasions) both the simplest strategy and the most successful in direct competition. An agent using this strategy will first cooperate, then subsequently replicate an opponent's previous action. If the opponent previously was cooperative, the agent is cooperative. If not, the agent is not.Wikipedia:Tit for tat Dot product might not be as strange as it first appears :See Hodge dual Let \overline{\mathbf{v}} be a covector (a pseudovector) that is the orthogonal complement of vector \mathbf{v}. (A covector would be a tensor of rank -1.) \mathbf{u} \bullet \mathbf{v} = \mathbf{u} \wedge \overline{\mathbf{v}}I = \begin{bmatrix} u_1 \mathbf{e_1} \\ u_2 \mathbf{e_2} \\ u_3 \mathbf{e_3} \end{bmatrix} \wedge \begin{bmatrix} v_1 \mathbf{e_2 e_3} & v_2 \mathbf{e_3 e_1} & v_3 \mathbf{e_1 e_2} \end{bmatrix} I Therefore: \mathbf{u} \bullet \mathbf{v} = \begin{bmatrix} u_1 v_1 \mathbf{e_1 \wedge e_2 e_3} & u_1 v_2 \mathbf{e_1 \wedge e_3 e_1} & u_1 v_3 \mathbf{e_1 \wedge e_1 e_2} \\ u_2 v_1 \mathbf{e_2 \wedge e_2 e_3} & u_2 v_2 \mathbf{e_2 \wedge e_3 e_1} & u_2 v_3 \mathbf{e_2 \wedge e_1 e_2} \\ u_3 v_1 \mathbf{e_3 \wedge e_2 e_3} & u_3 v_2 \mathbf{e_3 \wedge e_3 e_1} & u_3 v_3 \mathbf{e_3 \wedge e_1 e_2} \end{bmatrix} I Which reduces to: \mathbf{u} \bullet \mathbf{v} = \begin{bmatrix} u_1 v_1 \mathbf{e_{123}} & 0 & 0 \\ 0 & u_2 v_2 \mathbf{e_{123}} & 0 \\ 0 & 0 & u_3 v_3 \mathbf{e_{123}} \end{bmatrix} I = (u_1 v_1 + u_2 v_2 + u_3 v_3) Because a trivector in 3 dimensions is a pseudoscalar: : (\mathbf{e_{123}})I = 1 ::Trivector with unit volume. : (\mathbf{e_{223}})I = 0 ::Trivector with zero volume (since its 2 dimensional). So, just like the cross product, the dot product is the dual of the wedge product. But whereas the cross product is between two vectors the dot product is between a vector and a covector. The Regressive product is between 2 covectors. External links *http://mathinsight.org *https://math.stackexchange.com References This article incorporates text from Wikipedia:Category (mathematics) Category:Foundations